Top R Packages for Visualizing Table Data – Make Stunning Tables in Minutes {https://t.co/hiZC5N5X39} #rstats #DataScience
— R-bloggers (@Rbloggers) November 2, 2021
Data Science on Blockchain with R. Part II: Tracking the NFTs {https://t.co/d7Dz2ZfAWH} #rstats #DataScience
— R-bloggers (@Rbloggers) October 31, 2021
Announcing RStudio on Amazon SageMaker {https://t.co/SOk1q8IyjY} #rstats #DataScience
— R-bloggers (@Rbloggers) October 29, 2021
Scheduling Rmarkdown files on Windows – your foolproof guide {https://t.co/ASXiYPbUGx} #rstats #DataScience
— R-bloggers (@Rbloggers) November 2, 2021
Cross Validation in R with Example {https://t.co/HOTWPKgy7z} #rstats #DataScience
— R-bloggers (@Rbloggers) October 31, 2021
Regression in R-Ultimate Guide {https://t.co/3y5X2H3rjg} #rstats #DataScience
— R-bloggers (@Rbloggers) October 31, 2021
Smooth flow maps and a new edge bundling algorithm {https://t.co/zuXN8YRgks} #rstats #DataScience
— R-bloggers (@Rbloggers) November 1, 2021
What’s Neural Network? {https://t.co/gcWX70DDDZ} #rstats #DataScience
— R-bloggers (@Rbloggers) November 2, 2021
User authentication in R Shiny – sneak peek of shiny.users and shiny.admin packages {https://t.co/Cwro9q4uUi} #rstats #DataScience
— R-bloggers (@Rbloggers) November 2, 2021
Fast and scalable forecasting with ahead::ridge2f {https://t.co/P8OJ6xnK0U} #rstats #DataScience
— R-bloggers (@Rbloggers) November 1, 2021
Walmart’s 7-Year Nonlinear Market Trend using ‘Stealth Curves’ {https://t.co/3MShiCsgAH} #rstats #DataScience
— R-bloggers (@Rbloggers) October 29, 2021
Exclusive Lasso and Group Lasso using R code {https://t.co/dSBmm1daHj} #rstats #DataScience
— R-bloggers (@Rbloggers) October 29, 2021
Top R Packages for Visualizing Table Data – Make Stunning Tables in Minutes {https://t.co/hiZC5N5X39} #rstats #DataScience
— R-bloggers (@Rbloggers) November 2, 2021
Apache Arrow in R – Supercharge Your R Shiny Dashboards with 10X Faster Data Loading {https://t.co/a2tP47K86m} #rstats #DataScience
— R-bloggers (@Rbloggers) October 17, 2021
Descriptive Statistics in R {https://t.co/WYkwi8CCv4} #rstats #DataScience
— R-bloggers (@Rbloggers) October 21, 2021
How to Start a Career as an R Shiny Developer {https://t.co/iJkDCxgHe0} #rstats #DataScience
— R-bloggers (@Rbloggers) October 26, 2021
Data Normalization in R {https://t.co/EoZeUDNKCP} #rstats #DataScience
— R-bloggers (@Rbloggers) October 17, 2021
Descriptive Statistics in R {https://t.co/btum67rO6X} #rstats #DataScience
— R-bloggers (@Rbloggers) October 23, 2021
R Packages for Data Science {https://t.co/IT5qlRCK4N} #rstats #DataScience
— R-bloggers (@Rbloggers) October 16, 2021
A beginner’s guide to Shiny modules {https://t.co/IGWyHkw4pG} #rstats #DataScience
— R-bloggers (@Rbloggers) October 21, 2021
Getting started with network plots {https://t.co/ZEiuCsexDm} #rstats #DataScience
— R-bloggers (@Rbloggers) October 14, 2021
Data Science on Blockchain with R. Part II: Tracking the NFTs {https://t.co/d7Dz2ZfAWH} #rstats #DataScience
— R-bloggers (@Rbloggers) October 31, 2021
Tidy Time Series Forecasting in R with Spark {https://t.co/aYzVweiY2q} #rstats #DataScience
— R-bloggers (@Rbloggers) October 20, 2021
Model Selection in R (AIC Vs BIC) {https://t.co/XdpJQczdEv} #rstats #DataScience
— R-bloggers (@Rbloggers) October 28, 2021
---
title: "RBloggers Top Tweets"
output:
flexdashboard::flex_dashboard:
vertical_layout: scroll
source_code: embed
theme:
version: 4
bootswatch: yeti
css: styles/main.css
---
```{r setup, include=FALSE}
library(flexdashboard)
library(dplyr)
library(httr)
library(lubridate)
library(jsonlite)
library(purrr)
rbloggers <- fromJSON("data/rbloggers.json")
get_tweet_embed <- function(user, status_id) {
url <-
stringr::str_glue(
"https://publish.twitter.com/oembed?url=https://twitter.com/{user}/status/{status_id}&partner=&hide_thread=false"
)
response <- GET(url) %>%
content()
return(shiny::HTML(response$html))
}
```
Column {.tabset .tabset-fade}
-----------------------------------------------------------------------
### Top Tweets - 7 days {.tweet-wall}
```{r}
rblog_7 <- rbloggers %>%
mutate(created_at = as_date(created_at)) %>%
filter(created_at %within% interval(start = today() - 7, end = today())) %>%
slice_max(favorite_count + retweet_count, n = 12)
rblog_7_html <-
map2_chr(rblog_7$screen_name, rblog_7$status_id, get_tweet_embed)
shiny::HTML(stringr::str_glue("{rblog_7_html}"))
```
### Top Tweets - 30 days {.tweet-wall}
```{r}
rblog_30 <- rbloggers %>%
mutate(created_at = as_date(created_at)) %>%
filter(created_at %within% interval(start = today() - 30, end = today())) %>%
slice_max(favorite_count + retweet_count, n = 12)
rblog_30_html <-
map2_chr(rblog_30$screen_name, rblog_30$status_id, get_tweet_embed)
shiny::HTML(stringr::str_glue("{rblog_30_html}"))
```